Update DEFT2021.py

DEFT2021.py

@@ -1,7 +1,12 @@
 import os
+import re
+import ast
 import json
 import random
 from pathlib import Path
+from itertools import product
+from dataclasses import dataclass
+from typing import Dict, List, Tuple
 
 import datasets
 import numpy as np
@@ -46,37 +51,37 @@ class DEFT2021(datasets.GeneratorBasedBuilder):
     ]
 
     def _info(self):
+        
+        if self.config.name.find("cls") != -1:
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "specialities": datasets.Sequence(
+                        datasets.features.ClassLabel(names=_SPECIALITIES),
+                    ),
+                    "specialities_one_hot": datasets.Sequence(
+                        datasets.Value("float"),
+                    ),
+                }
+            )
 
-		if self.config.name.find("cls") != -1:
-
-          features = datasets.Features(
-              {
-                  "id": datasets.Value("string"),
-                  "document_id": datasets.Value("string"),
-                  "text": datasets.Value("string"),
-                  "specialities": datasets.Sequence(
-                      datasets.features.ClassLabel(names=_SPECIALITIES),
-                  ),
-                  "specialities_one_hot": datasets.Sequence(
-                      datasets.Value("float"),
-                  ),
-              }
-          )
-
-		elif self.config.name.find("ner") != -1:
-
-          features = datasets.Features(
-              {
-                  "id": datasets.Value("string"),
-                  "document_id": datasets.Value("string"),
-                  "tokens": datasets.Sequence(datasets.Value("string")),
-                  "ner_tags": datasets.Sequence(
-                      datasets.features.ClassLabel(
-                          names = ['O', 'B-anatomie', 'I-anatomie', 'B-date', 'I-date', 'B-dose', 'I-dose', 'B-duree', 'I-duree', 'B-examen', 'I-examen', 'B-frequence', 'I-frequence', 'B-mode', 'I-mode', 'B-moment', 'I-moment', 'B-pathologie', 'I-pathologie', 'B-sosy', 'I-sosy', 'B-substance', 'I-substance', 'B-traitement', 'I-traitement', 'B-valeur', 'I-valeur'],
-                      )
-                  ),
-              }
-          ),
+        elif self.config.name.find("ner") != -1:
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names = ['O', 'B-anatomie', 'I-anatomie', 'B-date', 'I-date', 'B-dose', 'I-dose', 'B-duree', 'I-duree', 'B-examen', 'I-examen', 'B-frequence', 'I-frequence', 'B-mode', 'I-mode', 'B-moment', 'I-moment', 'B-pathologie', 'I-pathologie', 'B-sosy', 'I-sosy', 'B-substance', 'I-substance', 'B-traitement', 'I-traitement', 'B-valeur', 'I-valeur'],
+                        )
+                    ),
+                }
+            ),
 
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -492,84 +497,91 @@ class DEFT2021(datasets.GeneratorBasedBuilder):
         return final_json
 
     def _generate_examples(self, data_dir, split):
+        
+        if self.config.name.find("cls") != -1:
 
-		if self.config.name.find("cls") != -1:
-          all_res = {}
-  
-          key = 0
-  
-          if split == 'train' or split == 'validation':
-              split_eval = 'train'
-          else:
-              split_eval = 'test'
-  
-          path_labels = Path(data_dir)  / 'evaluations' / f"ref-{split_eval}-deft2021.txt"
-  
-          with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:
-  
-              doc_specialities_ = {}
-              with open(path_labels) as f_spec:
-                  doc_specialities = [line.strip() for line in f_spec.readlines()]
-                  for raw in doc_specialities:
-                      raw_split = raw.split('\t')
-                      if len(raw_split) == 3 and raw_split[0] in doc_specialities_:
-                          doc_specialities_[raw_split[0]].append(raw_split[1])
-                      elif len(raw_split) == 3 and raw_split[0] not in doc_specialities_:
-                          doc_specialities_[raw_split[0]] = [raw_split[1]]
-  
-              ann_path = Path(data_dir) / "DEFT-cas-cliniques"
-  
-              for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):
-  
-                  ann_file = txt_file.with_suffix("").name.split('.')[0]+'.ann'
-  
-                  if ann_file in doc_specialities_:
-  
-                      res = {}
-                      res['document_id'] = txt_file.with_suffix("").name
-                      with txt_file.open() as f:
-                          res["text"] = f.read()
-  
-                      specialities = doc_specialities_[ann_file]
-  
-                      # Empty one hot vector
-                      one_hot = [0.0 for i in _SPECIALITIES]
-  
-                      # Fill up the one hot vector
-                      for s in specialities:
-                          one_hot[_SPECIALITIES.index(s)] = 1.0
-  
-                      all_res[res['document_id']] = {
-                          "id": str(key),
-                          "document_id": res['document_id'],
-                          "text": res["text"],
-                          "specialities": specialities,
-                          "specialities_one_hot": one_hot,
-                      }
-  
-                      key += 1
-  
-              distribution = [line.strip() for line in f_dist.readlines()]
-              
-              random.seed(4)
-              train = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'train 2021']
-              random.shuffle(train)
-              random.shuffle(train)
-              random.shuffle(train)
-              train, validation = np.split(train, [int(len(train)*0.7096)])
-              
-              test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']
-  
-              if split == "train":
-                  allowed_ids = list(train)
-              elif split == "test":
-                  allowed_ids = list(test)
-              elif split == "validation":
-                  allowed_ids = list(validation)
-  
-              for r in all_res.values():
-                  if r["document_id"]+'.txt' in allowed_ids:
-                      yield r["id"], r
+            all_res = {}
+
+            key = 0
+
+            if split == 'train' or split == 'validation':
+                split_eval = 'train'
+            else:
+                split_eval = 'test'
+
+            path_labels = Path(data_dir)  / 'evaluations' / f"ref-{split_eval}-deft2021.txt"
+
+            with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:
+
+                doc_specialities_ = {}
+
+                with open(path_labels) as f_spec:
+
+                    doc_specialities = [line.strip() for line in f_spec.readlines()]
+
+                    for raw in doc_specialities:
+
+                        raw_split = raw.split('\t')
+
+                        if len(raw_split) == 3 and raw_split[0] in doc_specialities_:
+                            doc_specialities_[raw_split[0]].append(raw_split[1])
+                        
+                        elif len(raw_split) == 3 and raw_split[0] not in doc_specialities_:
+                            doc_specialities_[raw_split[0]] = [raw_split[1]]
+
+                ann_path = Path(data_dir) / "DEFT-cas-cliniques"
+
+                for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):
+
+                    ann_file = txt_file.with_suffix("").name.split('.')[0]+'.ann'
+
+                    if ann_file in doc_specialities_:
+
+                        res = {}
+                        res['document_id'] = txt_file.with_suffix("").name
+                        with txt_file.open() as f:
+                            res["text"] = f.read()
+
+                        specialities = doc_specialities_[ann_file]
+
+                        # Empty one hot vector
+                        one_hot = [0.0 for i in _SPECIALITIES]
+
+                        # Fill up the one hot vector
+                        for s in specialities:
+                            one_hot[_SPECIALITIES.index(s)] = 1.0
+
+                        all_res[res['document_id']] = {
+                            "id": str(key),
+                            "document_id": res['document_id'],
+                            "text": res["text"],
+                            "specialities": specialities,
+                            "specialities_one_hot": one_hot,
+                        }
+
+                        key += 1
+
+                distribution = [line.strip() for line in f_dist.readlines()]
+                
+                random.seed(4)
+                train = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'train 2021']
+                random.shuffle(train)
+                random.shuffle(train)
+                random.shuffle(train)
+                train, validation = np.split(train, [int(len(train)*0.7096)])
+                
+                test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']
+
+                if split == "train":
+                    allowed_ids = list(train)
+                elif split == "test":
+                    allowed_ids = list(test)
+                elif split == "validation":
+                    allowed_ids = list(validation)
+
+                for r in all_res.values():
+                    if r["document_id"]+'.txt' in allowed_ids:
+                        yield r["id"], r
 
         elif self.config.name.find("ner") != -1: